Forrest spent a week on the run in southeast Mississippi last October. Breakthroughs, discoveries, and DIY tips sent every weekday. A rhesus macaque who spent a week on the lam in Mississippi in late October is finally settling into a new home over 990 miles from the original site of his escape. Popcorn Park Animal Refuge in Forked River, New Jersey, is now caring for Forrest, a young monkey from the Tulane National Primate Research Center in Covington, Louisiana. "The secret is out!" Popcorn Park posted to social media on December 2. Forrest's stressful saga began on October 28, 2025, when a transport truck crashed along Interstate 65 while carrying 21 monkeys from the Tulane Primate Research Center destined for a Florida biomedical research facility.

A significant number of traffic crashes are secondary crashes that occur because of an earlier incident on the road. Thus, early detection of traffic incidents is crucial for road users from safety perspectives with a potential to reduce the risk of secondary crashes. The wide availability of GPS devices now-a-days gives an opportunity of tracking and recording vehicle trajectories. The objective of this study is to use vehicle trajectory data for advance real-time detection of traffic incidents on highways using machine learning-based algorithms. The study uses three days of unevenly sequenced vehicle trajectory data and traffic incident data on I-10, one of the most crash-prone highways in Louisiana. Vehicle trajectories are converted to trajectories based on virtual detector locations to maintain spatial uniformity as well as to generate historical traffic data for machine learning algorithms. Trips matched with traffic incidents on the way are separated and along with other trips with similar spatial attributes are used to build a database for modeling. Multiple machine learning algorithms such as Logistic Regression, Random Forest, Extreme Gradient Boost, and Artificial Neural Network models are used to detect a trajectory that is likely to face an incident in the downstream road section. Results suggest that the Random Forest model achieves the best performance for predicting an incident with reasonable recall value and discrimination capability.

Flooding is one of the most disastrous natural hazards, responsible for substantial economic losses. A predictive model for flood-induced financial damages is useful for many applications such as climate change adaptation planning and insurance underwriting. This research assesses the predictive capability of regressors constructed on the National Flood Insurance Program (NFIP) dataset using neural networks (Conditional Generative Adversarial Networks), decision trees (Extreme Gradient Boosting), and kernel-based regressors (Gaussian Process). The assessment highlights the most informative predictors for regression. The distribution for claims amount inference is modeled with a Burr distribution permitting the introduction of a bias correction scheme and increasing the regressor's predictive capability. Aiming to study the interaction with physical variables, we incorporate Daymet rainfall estimation to NFIP as an additional predictor. A study on the coastal counties in the eight US South-West states resulted in an $R^2=0.807$. Further analysis of 11 counties with a significant number of claims in the NFIP dataset reveals that Extreme Gradient Boosting provides the best results, that bias correction significantly improves the similarity with the reference distribution, and that the rainfall predictor strengthens the regressor performance.

David Cearley, vice president and Gartner Fellow, wrote that promises of artificial intelligence (AI) magically performing intellectual tasks that humans do and dynamically learning as much as humans is "speculative at best." However with 2018 rapidly approaching, AI is clearly on the minds of many businesses. Where are businesses practically applying AI in their digital workplaces? In October 2017, Cearley noted at the Gartner 2017 Symposium/ITxpo in Orlando, FL that Narrow AI currently holds the most promise. Narrow AI is composed of "highly scoped machine-learning solutions that target a specific task (such as understanding language or driving a vehicle in a controlled environment) with algorithms chosen that are optimized for that task," he says.

David Cearley, vice president and Gartner Fellow, wrote that promises of artificial intelligence (AI) magically performing intellectual tasks that humans do and dynamically learning as much as humans is "speculative at best." However with 2018 rapidly approaching, AI is clearly on the minds of many businesses. Where are businesses practically applying AI in their digital workplaces? In October 2017, Cearley noted at the Gartner 2017 Symposium/ITxpo in Orlando, FL that Narrow AI currently holds the most promise. Narrow AI is composed of "highly scoped machine-learning solutions that target a specific task (such as understanding language or driving a vehicle in a controlled environment) with algorithms chosen that are optimized for that task," he says.